code: def forward(self, x, thw_shape=None):
      x_norm = self.norm1(x)
      x_block, thw_shape_new = self.attn(x_norm, thw_shape)
      if self.dim_mul_in_att and self.dim != self.dim_out:
          x = self.proj(x_norm)
      x_res, _ = attention_pool(
          x, self.pool_skip, thw_shape, has_cls_embed=self.has_cls_embed
      )
      if self.gamma_1 is not None:
          x = x_res + self.drop_path(self.gamma_1 * x_block)
      else:
          x = x_res + self.drop_path(x_block)
      x_norm = self.norm2(x)
      x_mlp = self.mlp(x_norm)
      if not self.dim_mul_in_att and self.dim != self.dim_out:
          x = self.proj(x_norm)
      if self.gamma_2 is not None:
          x = x + self.drop_path(self.gamma_2 * x_mlp)
      else:
          x = x + self.drop_path(x_mlp)
      if thw_shape:
          return x, thw_shape_new
      else:
          return x
  
file_path: SlowFast/slowfast/models/attention.py
imports: import numpy
  import torch
  import torch.nn
  import torch.nn.functional
  from torch.nn.init import trunc_normal_
  from slowfast.models.common import DropPath, Mlp
label_desc: Use image node_id 2 for calling the MultiScaleBlock obj's underlying member method code with example usage: obj.forward(x, thw_shape) and returns: x,  thw_shape_new, x